{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f52146b4-89fa-4b4f-97e1-ccf5a26c6d95",
   "metadata": {},
   "source": [
    "# Figure 4: Binomial Excess Visualization\n",
    "In this notebook, we provide the code to reproduce both panels of Figure 4 of the paper \"Evidence for Neutrino Emission from X-ray Bright Active Galactic Nuclei with IceCube\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b678fa2f-c74e-4048-a46b-f2fdc9bab5cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import binom\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams.update({\n",
    "    'text.usetex': True, # Comment out if matplotlib complains\n",
    "    'font.family': 'serif',\n",
    "    'font.size': 12,\n",
    "    'text.latex.preamble': r'\\usepackage{newtxtext,newtxmath}', # Comment out if matplotlib complains\n",
    "    'axes.labelsize': 'x-large',\n",
    "    'axes.labelpad':10,\n",
    "    'lines.linewidth': 3,\n",
    "    'lines.markersize': 10,\n",
    "    'xtick.labelsize':'x-large',\n",
    "    'ytick.labelsize':'x-large',\n",
    "    'legend.fontsize': 'x-large',\n",
    "    'legend.frameon': False,\n",
    "})\n",
    "\n",
    "#colors for plots\n",
    "blue_scale = [\"tab:blue\", \"#7ABAE5\", \"#B7D8F2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "75c81eee-9f5f-45e4-9c14-fbcdb8039209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unblinded catalogs\n",
    "unblinded_gammaray_floating_gamma = pd.read_csv('unblinded_gammaray_cat_floating_gamma.csv')\n",
    "unblinded_seyfert_floating_gamma = pd.read_csv('unblinded_xray_bright_cat.csv')\n",
    "# Mask to exclude NGC 1068\n",
    "mask_ngc1068 = unblinded_seyfert_floating_gamma['Source Name'] != 'NGC1068'\n",
    "unblinded_seyfert_floating_gamma = unblinded_seyfert_floating_gamma[mask_ngc1068]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c992003",
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_binomial_test(test_hypo):\n",
    "    \"\"\"Perform the binomial test for a given hypothesis dictionary.\"\"\"\n",
    "\n",
    "    # Observed p-values thresholds\n",
    "    logp_thr_obs = np.sort(test_hypo['-log10pval'])\n",
    "\n",
    "    # BINOMIAL TEST\n",
    "    # Calculate number of observed p-values above each threshold\n",
    "    counts_obs = np.asarray([np.sum(logp_thr_obs>=logp) for logp in logp_thr_obs])\n",
    "    # Calculate the binomial p-values through the CDF\n",
    "    binom_pValues = 1 - binom.cdf(counts_obs-1, len(test_hypo), 10**(-logp_thr_obs))\n",
    "    # Select best-fit, i.e., minium observed binomial p-value\n",
    "    best_idx = np.argmin(binom_pValues[:-1])\n",
    "    best_fit = {\n",
    "        'log10p_thres': logp_thr_obs[best_idx],\n",
    "        'n_observed': counts_obs[best_idx],\n",
    "    }\n",
    "\n",
    "    return logp_thr_obs, counts_obs, binom_pValues, best_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9fbe254b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_plot(\n",
    "        logp_thr_obs, counts_obs, binom_pValues, best_fit, bkg_expectation, \n",
    "        x_cut_left, x_cut_right, alpha=0.7, bf_color='red'):\n",
    "    fig, axes = plt.subplots(\n",
    "        2, 2, figsize=(6,4.5), sharex= 'col',\n",
    "        gridspec_kw = {'height_ratios':[2, 1], 'width_ratios':[8,1]} )\n",
    "    plt.subplots_adjust( hspace=0 , wspace=0.07)\n",
    "\n",
    "    ax = axes[0][0]\n",
    "    ax1 = axes[1][0]\n",
    "    ax2 = axes[0][1]\n",
    "    ax3 = axes[1][1]\n",
    "\n",
    "    # Plot background expectation\n",
    "    ax.plot(bkg_expectation[1], 10**(-bkg_expectation[0]), dashes=(5,3), color='k', lw=2, zorder=7, label='Bkg Expectation')\n",
    "    ax2.plot(bkg_expectation[1], 10**(-bkg_expectation[0]), dashes=(5,3), color='k', lw=2, zorder=7,)\n",
    "\n",
    "    # Plot error regions\n",
    "    cl = []\n",
    "    std_dev = np.sqrt(bkg_expectation[1]) # Poissonan uncertainty\n",
    "    for c in [1,2,3]:\n",
    "        # Define 1, 2, and 3 sigma unc. bands\n",
    "        cl.append((std_dev**2-c*std_dev, std_dev**2+c*std_dev))\n",
    "\n",
    "    ax.fill_betweenx(10**(-bkg_expectation[0]), cl[2][1], cl[2][0], alpha=alpha, color=blue_scale[2], zorder=2)\n",
    "    ax.fill_betweenx(10**(-bkg_expectation[0]), cl[1][1], cl[1][0], alpha=alpha, color=blue_scale[1], zorder=4)\n",
    "    ax.fill_betweenx(10**(-bkg_expectation[0]), cl[0][1], cl[0][0], alpha=alpha, color=blue_scale[0], zorder=6)\n",
    "    ax2.fill_betweenx(10**(-bkg_expectation[0]), cl[2][1], cl[2][0], alpha=alpha, color=blue_scale[2], zorder=2)\n",
    "    ax2.fill_betweenx(10**(-bkg_expectation[0]), cl[1][1], cl[1][0], alpha=alpha, color=blue_scale[1], zorder=4)\n",
    "    ax2.fill_betweenx(10**(-bkg_expectation[0]), cl[0][1], cl[0][0], alpha=alpha, color=blue_scale[0], zorder=6)\n",
    "\n",
    "    # Plot the observed counts selected to be plotted\n",
    "    mask = counts_obs < x_cut_left\n",
    "    ax.scatter(counts_obs[mask], 10**(-logp_thr_obs[mask]),\n",
    "            marker='o', facecolors='k', edgecolors='w',\n",
    "            lw=0.5, s=20, zorder=8, label='Observation')\n",
    "    # And add the last ordered p-values\n",
    "    mask = counts_obs > x_cut_right\n",
    "    ax2.scatter(counts_obs[mask], 10**(-logp_thr_obs[mask]),\n",
    "            marker='o', facecolors='k', edgecolors='w',\n",
    "            lw=0.5, s=20, zorder=8)\n",
    "    \n",
    "    # Plot binomial p-values\n",
    "    ax1.plot(counts_obs, binom_pValues, color='k', zorder=8)\n",
    "    ax3.plot(counts_obs, binom_pValues, color='k')\n",
    "\n",
    "    # Plot best-fit lines\n",
    "    ax.hlines(10**(-best_fit['log10p_thres']), xmin=0, xmax=best_fit['n_observed'],\n",
    "            color=bf_color, linestyle=':', label='Max deviation', zorder=7)\n",
    "    ax.vlines(best_fit['n_observed'], ymin=10**(-best_fit['log10p_thres']), ymax=1,\n",
    "            color=bf_color, linestyle=':', zorder=7)\n",
    "    ax1.axvline(best_fit['n_observed'], color=bf_color, linestyle=':', zorder=7)\n",
    "\n",
    "    # Some other plotting adjustments (for better visualization)\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax1.set_yscale('log')\n",
    "    ax2.set_yscale('log')\n",
    "    ax3.set_yscale(\"log\")\n",
    "    ax1.set_yticks([1e-5, 1e-3, 1e-1])\n",
    "    ax3.set_yticks([1e-5, 1e-3, 1e-1])\n",
    "    ax1.set_yticklabels([r'10$^{-5}$', r'10$^{-3}$', r'10$^{-1}$'])\n",
    "    ax.set_ylim(5e-8, 1.7)\n",
    "    ax2.set_ylim(5e-8, 1.7)\n",
    "    ax1.set_ylim(min(binom_pValues)*.3, 3)\n",
    "    ax3.set_ylim(min(binom_pValues)*.3, 3)\n",
    "    ax.invert_yaxis()\n",
    "    ax2.invert_yaxis()\n",
    "    ax.spines.right.set_visible(False)\n",
    "    ax1.spines.right.set_visible(False)\n",
    "    ax2.spines.left.set_visible(False)\n",
    "    ax3.spines.left.set_visible(False)\n",
    "    ax2.tick_params(axis='y', colors='w')\n",
    "    ax3.set_yticklabels([])\n",
    "    ax3.tick_params(axis='y', colors='w')\n",
    "    ax2.set_yticklabels([])\n",
    "    ax3.set_xticks([max(counts_obs)])\n",
    "    ax.set_xlim(-0.2, x_cut_left+2)\n",
    "    ax1.set_xlim(-0.2, x_cut_left+2)\n",
    "    ax2.set_xlim(x_cut_right-1, counts_obs[0]+.5)\n",
    "    ax3.set_xlim(x_cut_right-1, counts_obs[0]+.5)\n",
    "\n",
    "    d = 1.5 # proportion of vertical to horizontal extent of the slanted line\n",
    "    kwargs = dict(marker=[(-1, -d), (1, d)], markersize=10, lw=1,\n",
    "              linestyle=\"none\", color='k', mec='k', mew=1, clip_on=False)\n",
    "    ax.plot([1, 1], [0, 1], transform=ax.transAxes, **kwargs)\n",
    "    ax1.plot([1, 1], [0, 1], transform=ax1.transAxes, **kwargs)\n",
    "    ax2.plot([0, 0], [1, 0], transform=ax2.transAxes, **kwargs)\n",
    "    ax3.plot([0, 0], [1, 0], transform=ax3.transAxes, **kwargs)\n",
    "    \n",
    "    for axis in axes.flatten():\n",
    "        axis.grid(axis='both', ls=':', which=\"major\", alpha=0.6, zorder=0)\n",
    "\n",
    "    # Add labels\n",
    "    ax1.set_xlabel(r\"$N$ Sources\")\n",
    "    ax1.xaxis.set_label_coords(.6,-.5)\n",
    "    ax.set_ylabel(r\"$p_\\mathrm{local}$\")\n",
    "    ax1.set_ylabel(r\"$p_\\mathrm{binom}$\", x=ax.yaxis.label.get_unitless_position())\n",
    "    \n",
    "    #Add legend\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(handles, labels, ncol=1,\n",
    "              loc='upper right', bbox_to_anchor=(1.056,.9),\n",
    "              handlelength=1.6, handletextpad=.8)\n",
    "\n",
    "    return ax, ax1, ax2, ax3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8decf7f-52ba-4511-91e7-069a078e32fb",
   "metadata": {},
   "source": [
    "# Binomial test excess visualization for the 110 gamma-ray emitters under the power-law signal hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0f1c6681-ee5a-4501-a171-e5c85e0baa10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of sources considered for the binomial test is: 110\n"
     ]
    }
   ],
   "source": [
    "print(\"The total number of sources considered for the binomial test is:\",\n",
    "      len(unblinded_gammaray_floating_gamma))\n",
    "\n",
    "# Load background expectation\n",
    "gammaray_bkg_expectation = np.loadtxt(\n",
    "    './bkg_expectation_gammaray_withNGC1068.csv', delimiter=',', skiprows=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06d6645f-f4fb-4d5e-8bab-54c5352967ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract quantities for the binomial test\n",
    "logp_thr_obs, counts_obs, binom_pValues, best_fit = do_binomial_test(\n",
    "    test_hypo=unblinded_gammaray_floating_gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3cb9c24f-7d00-4d85-966a-e0bb07b7db1a",
   "metadata": {
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x450 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the plot\n",
    "x_cut_left, x_cut_right = 55.5, 109.5  # for visualization\n",
    "ax, ax1, ax2, ax3 = make_plot(\n",
    "    logp_thr_obs, counts_obs, binom_pValues, best_fit, gammaray_bkg_expectation,\n",
    "    x_cut_left, x_cut_right)\n",
    "\n",
    "# Highlight top 3 sources\n",
    "ax.text(2.05, 10**(-logp_thr_obs[-1])*1.5, r'NGC$\\,1068$', fontsize=15)\n",
    "ax.text(3, 10**(-logp_thr_obs[-2])*1.5, r'PKS$\\,1424$+$240$', fontsize=15)\n",
    "ax.text(4, 10**(-logp_thr_obs[-3])*1.5, r'TXS$\\,0506$+$056$', fontsize=15)\n",
    "\n",
    "# Add custom xticks\n",
    "ax1.set_xticks(np.arange(3, x_cut_left, 10))\n",
    "ax.set_xticks(np.arange(3, x_cut_left, 10))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f6c164-9a76-417d-9aaa-5f983fcd0d05",
   "metadata": {},
   "source": [
    "# Binomial Test Visualization for the 47 X-ray bright AGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c138493c-22d4-402f-b564-b54bc0aad860",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of sources considered for the binomial test is: 47\n"
     ]
    }
   ],
   "source": [
    "print(\"The total number of sources considered for the binomial test is:\",\n",
    "      len(unblinded_seyfert_floating_gamma))\n",
    "\n",
    "# Load background expectation\n",
    "xray_bright_bkg_expectation = np.loadtxt(\n",
    "    './bkg_expectation_xray_bright_withoutNGC1068.csv', delimiter=',', skiprows=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "51680b62-f71d-4f84-ad11-74320783024b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract quantities for the binomial test\n",
    "logp_thr_obs, counts_obs, binom_pValues, best_fit = do_binomial_test(\n",
    "    test_hypo=unblinded_seyfert_floating_gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74362f83-5179-478e-8573-42183d87c170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x450 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_cut_left, x_cut_right = 33.5, 46.5  # for visualization\n",
    "# Make the plot\n",
    "ax, ax1, ax2, ax3 = make_plot(\n",
    "    logp_thr_obs, counts_obs, binom_pValues, best_fit,\n",
    "    xray_bright_bkg_expectation, x_cut_left, x_cut_right)\n",
    "\n",
    "ax.scatter(0.1, 2.231723e-07,\n",
    "           lw=.5, marker='*', facecolors='gold', edgecolors='k',\n",
    "           s=20, zorder=8)\n",
    "ax.scatter(0.1, 2.231723e-07,\n",
    "           lw=.5, marker='*', facecolors='none', edgecolors='w',\n",
    "           s=20, zorder=7)\n",
    "ax.text(1, 2.231723e-07*1.5, r'NGC$\\,1068$ (excluded)', fontsize=15)\n",
    "\n",
    "# Add custom xticks\n",
    "ax1.set_xticks(np.arange(0, x_cut_left, 11))\n",
    "ax.set_xticks(np.arange(0, x_cut_left, 11))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3191bdd6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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